Prediction on the residual strength of oil and gas pipelines based on PCA-SVR model

In order to predict the residual strength of single-defect oil and gas pipelines correctly, the relevant factors affecting the residual strength of pipelines were analyzed thoroughly. The basic principles and combination processes of principal component analysis (PCA), support vector regression(SVR)and PCA+SVR were introduced. Then, according to the data related to the single-defect pipelines acquired from the literature, the dimension of influential factors was reduced by PCA. Finally, the residual strength was predicted in SVR model, and the prediction results were compared with the results of other common models and calculation methods to verify the feasibility of PCA-SVR model. It is indicated that among all influential factors, the pipeline steel grade has the greatest influence on the residual strength of oil and gas pipelines. In addition, the average prediction error of PCA+SVR prediction model is 1.91% and the root mean square error is 0.34, indicating higher accuracy of this method. However, all of the prediction results are lower than the actual residual strength, which proves that this method is relatively conservative and can decrease the operating efficiency of pipelines.